Variable selection techniques after multiple imputation in high-dimensional data

Faisal Maqbool Zahid; Shahla Faisal; Christian Heumann

doi:10.1007/s10260-019-00493-7

ScienceGate Book Chapters

JOURNAL ARTICLE

Variable selection techniques after multiple imputation in high-dimensional data

Faisal Maqbool Zahid Shahla Faisal Christian Heumann

Year: 2019 Journal: Statistical Methods & Applications Vol: 29 (3)Pages: 553-580 Publisher: Springer Science+Business Media

DOI: 10.1007/s10260-019-00493-7

Get Full-Text PDF Get Analytical Report

Keywords:

Missing data Imputation (statistics) Feature selection Computer science Data mining Selection (genetic algorithm) Variable (mathematics) Statistics Artificial intelligence Machine learning Mathematics

Metrics

Cited By

1.07

FWCI (Field Weighted Citation Impact)

Refs

0.78

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Bayesian Methods and Mixture Models

Physical Sciences → Computer Science → Artificial Intelligence

Variable selection techniques after multiple imputation in high-dimensional data

Metrics

Citation History

Topics

Related Documents

Multiple imputation in high-dimensional data with variable selection

Multiple imputation with compatibility for high-dimensional data

Integrative analysis and variable selection with multiple high-dimensional data sets

Variable selection for additive models with missing data via multiple imputation

Multiple imputation in the presence of high-dimensional data